2016/03/21 by Bernd Bohnet, Bohnet, Bernd, Miguel Ballesteros +5
Computer Science · #Computation and Language (cs.CL) #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Natural Language Processing Techniques #Rough Sets and Fuzzy Logic
paper · pdf · doi:10.48550/arxiv.1603.06503
openalex publication_date 2016/03/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study the use of greedy feature selection methods for morphosyntactic tagging under a number of different conditions. We compare a static ordering of features to a dynamic ordering based on mutual information statistics, and we apply the techniques to standalone taggers as well as joint systems for tagging and parsing. Experiments on five languages show that feature selection can result in more compact models as well as higher accuracy under all conditions, but also that a dynamic ordering works better than a static ordering and that joint systems benefit more than standalone taggers. We also show that the same techniques can be used to select which morphosyntactic categories to predict in order to maximize syntactic accuracy in a joint system. Our final results represent a substantial improvement of the state of the art for several languages, while at the same time reducing both the number of features and the running time by up to 80% in some cases.